Notes tagged with "papers": Nonlinear Function

17 notes tagged with "papers"

A Universal Law of Robustness via Isoperimetry

Link: A Universal Law of Robustness via Isoperimetry | OpenReview This paper purports to explain (and quantify) the observed fact that…

Tagged with: #papers#machine-learning

Convolutional Networks on Graphs for Learning Molecular Fingerprints

paper 2015 Existing systems for extracting features ('fingerprints') from molecules look a lot like convnets. They repeatedly apply a hash…

Tagged with: #papers#chemistry

Differentiable Particle Filtering without Modifying the Forward Pass

https://arxiv.org/abs/2106.10314 In Sequential Monte Carlo, we can resample with any set of weights, as long as we then initialize the new…

Tagged with: #papers#bayes

Neural message passing for Quantum Chemistry

Gilmer at al. paper 2017 Experiments on QM9. Unlike SMILES strings, includes molecular geometry . General formulation of message passing…

Tagged with: #chemistry#machine-learning#papers

Theorem Proving in Lean

Notes from working through Kevin Buzzard's Natural number game (imperial.ac.uk) using the Lean theorem prover. We know from the [ Curry…

Tagged with: #math#papers

Transformer Papers

Massive list here: https://github.com/cedrickchee/awesome-bert-nlp Bahdanau, Cho, Bengio. Neural Machine Translation by Jointly Learning to…

Tagged with: #machine-learning#papers

classic papers

More Is Different (kit.edu) On proof and progress in mathematics (Thurston, 1994) On Being the Right Size (Haldane, 1928))

Tagged with: #papers#fundamental

continuous structure learning

Relevant papers: DIfferentiable compositional kernel learning for Gaussian Processes (Sun et al., 2018) Differentiable Architecture Search…

Tagged with: #papers#machine-learning#bayes

decision transformer

paper: Chen, Lu, et al. 2021, https://arxiv.org/abs/2106.01345 Trajectories are represented as sequences: where is the return-to-go, i.e…

Tagged with: #ai#reinforcement-learning#papers

delayed sampling

Like quantum mechanics! We build up a distribution over variables defined so far. When we need to use a value, we sample from this…

Tagged with: #papers#bayes

nested SMC

Christian Naesseth, Fredrik Lindsten, Thomas Schon (2015): http://proceedings.mlr.press/v37/naesseth15.html The main idea: In an SMC…

Tagged with: #machine-learning#papers

noisy natural gradient as VI

https://arxiv.org/abs/1712.02390 Basic idea: optimizers like Adam and RMSProp already keep track of posterior curvature estimates. These are…

Tagged with: #machine-learning#papers

papers to read

Tagged with: #papers

previously read

AI / RL Distributional RL book: https://www.distributional-rl.org/ Alignment Sequences: Value learning: https://www.alignmentforum.org/s…

Tagged with: #papers#ideas

probabilistic transformers

A short note on interpreting a transformer layer as performing maximum-likelihood inference in a Gaussian mixture model: https://arxiv.org…

Tagged with: #papers#machine-learning

provably safe system

References: Tegmark and Omohundro, Provably safe systems: the only path to controllable AGI (2023). https://arxiv.org/abs/2309.01933 they…

Tagged with: #papers#computer-science#crypto

reading inbox

In no particular order. Items may move to [ previously read ] if I read them or former reading inbox if I decide I'm not currently…

Tagged with: #papers#ideas

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